Attaining sufficient maturation and biomimetism to resemble adult myocardium remains a key challenge in cardiac tissue engineering. Here, we consider a multi-omic approach, including scRNA-seq and Spatial Transcriptomics (Visium technology), to improve our understanding of 3D engineered heart tissues (EHTs) generated using Melt Electrowriting and hiPSC-derived cardiomyocytes (hiPSC-CMs). We obtained datasets from hiPSC-CMs and cardiac fibroblasts (hiPSC-CFs) at day 0, and their combination in a 9:1 proportion in 3D EHTs cultured for 30 days. We optimized the downstream computational workflow, primarily using Seurat, and identified differential gene regulatory networks (GRN) via SCENIC and the machine learning algorithm SimiC. Subsequently, we collected and curated embryonic human cardiac datasets to benchmark our EHTs, define gene signatures for various cardiac phenotypes, and identify corresponding developmental stages.

We uncovered cellular heterogeneity within the initial hiPSC-CMs, including an off-target fibroblast population that persists after 30 days in EHTs, while most of the initially seeded hiPSC-CFs disappear. Cardiomyocytes at day 30 resemble in vivo embryonic ventricular and pacemaker phenotypes, showing features of maturation relative to day 0. However, GRN analysis revealed that these EHTs are closer to the earliest in vivo reference stages in terms of maturity. Spatial transcriptomics identified a uniform distribution of cardiomyocyte phenotypes while displaying an enrichment of fibroblasts near scaffold fibers.

This study highlights the power of integrating single-cell, spatial transcriptomics and computational approaches to resolve tissue heterogeneity, cell-type distribution and to identify the key targets required to advance in tissue design, maturation and resemble adult myocardium with higher fidelity.

Co-authors: Olalla Iglesias-Garcia, Eduardo Larequi, Ilazki Anaut-Lusar, Sarai Sarvide, Patxi San Martin-Uriz, Paula Aguirre-Ruiz, Felipe Prosper, Manuel M. Mazo Vega, Asier Ullate-Agote